Abstract The rapid urbanization and population growth of the 21st century have led to a significant increase in solid waste generation, placing immense pressure on municipal waste management systems. Traditional waste handling methods often lack efficiency, transparency, and scalability, resulting in environmental degradation, public health hazards, and economic losses. The integration of Artificial Intelligence (AI) and Computer Vision in waste management offers transformative solutions that optimize waste collection, automate sorting, and support data-driven policy decisions. This paper explores the technological foundations of smart waste management systems, focusing on AI algorithms for waste classification, object detection, route optimization, and behavior prediction. It highlights key use cases including intelligent waste bins, automated recycling facilities, and municipal fleet management. Real-world case studies demonstrate the effectiveness of AI-powered waste infrastructure in enhancing sustainability and operational efficiency. Ethical considerations such as data privacy, job displacement, and environmental justice are also critically analyzed. The paper concludes by addressing the challenges of infrastructure readiness, model generalizability, and public participation, while also examining future directions in edge computing, IoT integration, and circular economy analytics. Smart waste management powered by AI and computer vision holds the potential to build cleaner, greener, and more resilient urban ecosystems